DAE architecture is divided into three layers, namely input, encoding and decoding layer. In DAE, input and output should have the same dimension as we are constructing the input. Denoising Autoencoders(DAEs) with Dense Neural Network (DNN), Convolution Neural Network (CNN) [1]} and Long Short Term Memory (LSTM) [2]} were used for testing of noise filtering capabilities in ECG. DAE model are shown in figure 3.
<FIGURE>\(\theta \psi = argmin (\frac{1}{n} \sum _{i=1}^{n} L(x_i,z_i))\) 
\(= argmin (\frac{1}{n} \sum _{i=1}^{n} L(x_i,g_ \psi (f_\theta (x_i)))\) 
